DESCRIPTION (Applicant's abstract): There has been a great deal of progress in understanding how complex objects, in particular, faces, are processed by humans. The aim of this proposal is to extend recent work on computational models of emotion and face processing to account for recent data concerning face processing in brain-damaged patients and normal controls. In all of these studies, the goal is to understand to what extent face processing must be considered "special", through modeling and experiment. Our specific goals are to: I) analyze the relationship between categorical perception of emotion in our models and in human subjects as elucidated by parallel experiments on the models and humans using identical stimuli; 2) understand further how specialized "modules" may arise for face, object and expression recognition through combinations of competitive learning mechanisms and innate biases; 3) understand the effects of various kinds of 'brain damage" on our model, as a route to understanding patient behavior. These goals will be addressed through careful modeling studies guided by the data. Our hypotheses will be tested by constructing neural network models that process the same face images used in human studies.